Spatio-Temporal Evolution, Prediction and Optimization of LUCC Based on CA-Markov and InVEST Models: A Case Study of Mentougou District, Beijing
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. LUCC Classification and Quantification of Landscape Patterns
2.2.1. LUCC Transfer Matrix
2.2.2. Statistical Analysis
2.3. AHP and CA-Markov
2.4. Methods to Assessment ES
2.4.1. Water Yield
2.4.2. Soil Conservation and Soil Loss
2.4.3. Carbon Stocks
3. Results
3.1. Transfer Characteristics of LUCC and Landscape Patterns
3.1.1. Temporal and Spatial Transfer Characteristics of LUCC
3.1.2. Evolution Characteristics of Landscape Patterns of LUCC
3.2. Spatial Distribution of ES and Eco-Environmental Suitability of LUCC
3.2.1. ES of Mentougou District in 2014
3.2.2. Spatial Distribution and Quantitative Structure of Eco-Environment
3.2.3. Spatial Distribution Characteristics of Suitable LUCC
3.3. Comparative Results of LUCC and ES under the two Scenarios
3.3.1. LUCC Prediction and Optimization Results in 2030
3.3.2. Comparison of the ES of Present, Prediction and Optimization
4. Discussion
4.1. Impacts of Human Activities and Policies on LUCC
4.2. The Impact of Landscape Patterns Changes
4.3. Spatial Suitability of Various Types of LUCC in the Region
4.4. Impacts of Future LUCC on ES
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Appendix A
Types | Abbreviation | Content |
---|---|---|
Land use and land coverage changes (LUCC) | BUL | Built-up land |
OC | Orchard | |
WB | Water bodies | |
CL | Cropland | |
GL | Grassland | |
WL | Wasteland | |
BL | Bareland | |
FL | Forestland | |
SL | Shrubland | |
Landscape metrics (LM) | NP | Number patches |
PD | Patch density | |
MPS | Mean Patch Area | |
COHESION | Patch Cohesion Index | |
Ecosystem service (ES) | WY | Water yield |
SLO | Soil Loss | |
SC | Soil conservation | |
CS | Carbon stocks |
BUL | OC | WB | CL | GL | WL | BL | FL | SL | |
---|---|---|---|---|---|---|---|---|---|
BUL | 65.16 | ||||||||
OC | 1.37 | 44.99 | 8.41 | 2.4 | |||||
WB | 18.2 | 0.45 | |||||||
CL | 3.07 | 23.01 | 3.98 | ||||||
GL | 17.07 | ||||||||
WL | 0.43 | 2.06 | 7.18 | 82.02 | 1.93 | 32.09 | |||
BL | 0.98 | 1.11 | |||||||
FL | 599.58 | ||||||||
SL | 0.8 | 538.59 |
BUL | OC | WB | CL | GL | WL | BL | FL | SL | |
---|---|---|---|---|---|---|---|---|---|
BUL | 70.03 | ||||||||
OC | 0.83 | 34.13 | 0.87 | 7.86 | 1.3 | ||||
WB | 18.2 | ||||||||
CL | 0.4 | 20.52 | 4.15 | ||||||
GL | 18.01 | 0.28 | 6.94 | ||||||
WL | 0.14 | 5.29 | 2.46 | 0.62 | 68.6 | 8.36 | 9.39 | ||
BL | 0.45 | 2.59 | |||||||
FL | 0.54 | 600.7 | 33.63 | ||||||
SL | 1.84 | 3.76 | 532.99 |
BUL | OC | WB | CL | GL | WL | BL | FL | SL | |
---|---|---|---|---|---|---|---|---|---|
BUL | 71.39 | ||||||||
OC | 32.01 | 2.75 | 4.66 | ||||||
WB | 20.04 | ||||||||
CL | 6.52 | 0.17 | 17.17 | 0.51 | |||||
GL | 12.59 | 6.49 | |||||||
WL | 1.07 | 5.14 | 68.95 | 1.08 | 4.38 | ||||
BL | 0.06 | 2.53 | |||||||
FL | 563.65 | 49.46 | |||||||
SL | 2.29 | 0.01 | 9.92 | 572.04 |
BUL | OC | WB | CL | GL | WL | BL | FL | SL | |
---|---|---|---|---|---|---|---|---|---|
BUL | 78.98 | ||||||||
OC | 32.18 | ||||||||
WB | 18.76 | 3.57 | |||||||
CL | 2.49 | 4.02 | 15.8 | ||||||
GL | 12.65 | ||||||||
WL | 0.48 | 6.31 | 0.39 | 58.13 | 12.89 | ||||
BL | 2.21 | 0.32 | |||||||
FL | 554.01 | 20.63 | |||||||
SL | 0.91 | 630.15 |
BUL | OC | WB | CL | GL | WL | BL | FL | SL | |
---|---|---|---|---|---|---|---|---|---|
BUL | 81.96 | ||||||||
OC | 38.03 | 4.48 | |||||||
WB | 16.34 | 1.05 | 1.37 | ||||||
CL | 2.32 | 2.29 | 7.37 | 3.82 | |||||
GL | 15.78 | 0.82 | |||||||
WL | 0.39 | 16.77 | 0.77 | 36.11 | 4.09 | ||||
BL | 0.85 | 1.36 | |||||||
FL | 502.31 | 52.61 | |||||||
SL | 51.01 | 612.98 |
BUL | OC | WB | CL | GL | WL | BL | FL | SL | |
---|---|---|---|---|---|---|---|---|---|
BUL | 84.67 | ||||||||
OC | 53.19 | 0.74 | 3.16 | ||||||
WB | 16.17 | 0.17 | |||||||
CL | 1.6 | 7.59 | |||||||
GL | 16.89 | 1.11 | |||||||
WL | 0.02 | 0.28 | 1.6 | 0.66 | 23.88 | 9.67 | |||
BL | 0.14 | 1.22 | |||||||
FL | 553.32 | ||||||||
SL | 0.29 | 3.22 | 675.29 |
BUL | OC | WB | CL | GL | WL | BL | FL | SL | |
---|---|---|---|---|---|---|---|---|---|
BUL | 65.16 | ||||||||
OC | 2.58 | 39.59 | 1.77 | 13.23 | |||||
WB | 0.62 | 16.42 | 0.05 | 0.94 | 0.62 | ||||
CL | 15.49 | 2.51 | 7.98 | 0.12 | 3.96 | ||||
GL | 13.79 | 3.28 | |||||||
WL | 2.42 | 11.11 | 0.13 | 1.73 | 23.3 | 0.37 | 2.64 | 84.01 | |
BL | 1.24 | 0.85 | |||||||
FL | 479.12 | 120.46 | |||||||
SL | 0.03 | 74.79 | 464.57 |
Scenario_2 | CL | OC | FL | SHL | GL | WB | BUL | WL | BL |
---|---|---|---|---|---|---|---|---|---|
Quantitative of LUCC | 9.93 | 53.21 | 579.45 | 689.23 | 0.19 | 22.33 | 99.32 | 0.00 | 1.22 |
Factors | Types | Area (km2) | Percentage (%) | Suitability Evaluation | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Cropland | Orchard | Forestland | Grassland | Shrubland | Legend | |||||
Slope(°) | ≤5 | 107.59 | 7.4 | 4 | 4 | 4 | 4 | 4 | High | |
5~15 | 386.62 | 26.57 | 3 | 3 | 4 | 4 | 4 | |||
15~25 | 475.07 | 32.65 | 0 | 2 | 3 | 3 | 4 | |||
≥25 | 485.6 | 33.38 | 0 | 0 | 2 | 0 | 2 | |||
Aspect(°) | 0° | 6.48 | 0.45 | 4 | 4 | 4 | 4 | 4 | Low | |
0°–45° | 194.21 | 13.35 | 2 | 1 | 4 | 3 | 4 | |||
45°–135° | 392.64 | 26.99 | 3 | 3 | 3 | 4 | 3 | |||
135°–225° | 363.82 | 25.01 | 4 | 4 | 4 | 4 | 4 | |||
225°–315° | 324.13 | 22.28 | 3 | 3 | 3 | 4 | 3 | |||
315°–360° | 173.61 | 11.93 | 2 | 1 | 4 | 3 | 4 | |||
Elevation(m) | ≤200 | 85 | 5.84 | 4 | 4 | 4 | 4 | 4 | ||
200~500 | 333.15 | 22.9 | 3 | 3 | 4 | 4 | 4 | |||
≥500 | 1036.73 | 71.26 | 1 | 2 | 4 | 4 | 4 | |||
Soil type | Soilless area | 14.48 | 1 | 0 | 0 | 0 | 0 | 0 | ||
Cinnamon soil | 1136.32 | 78.1 | 3 | 3 | 4 | 4 | 4 | |||
Brown soil | 298.93 | 20.55 | 4 | 4 | 4 | 4 | 4 | |||
Moisture soil | 0.88 | 0.06 | 4 | 4 | 4 | 4 | 4 | |||
Meadow soil | 4.27 | 0.29 | 0 | 3 | 2 | 4 | 2 | |||
Soil Conservation(t/hm2) | ≤200 | 140.06 | 9.63 | 3 | 3 | 4 | 4 | 4 | ||
200~400 | 738.19 | 50.74 | 2 | 2 | 4 | 4 | 4 | |||
400~600 | 543 | 37.32 | 1 | 1 | 4 | 3 | 4 | |||
≥600 | 33.64 | 2.31 | 0 | 0 | 4 | 2 | 4 | |||
Water Yield(m3/hm2) | ≤500 | 84.63 | 5.82 | 0 | 0 | 0 | 2 | 1 | ||
500~1000 | 361.03 | 24.82 | 1 | 2 | 2 | 3 | 2 | |||
1000~1500 | 375.98 | 25.84 | 4 | 3 | 3 | 3 | 3 | |||
≥1500 | 633.24 | 43.53 | 4 | 4 | 4 | 4 | 4 | |||
Soil loss(t/hm2) | ≤5 | 981.68 | 67.48 | 4 | 4 | 4 | 4 | 4 | ||
5~10 | 424.09 | 29.15 | 3 | 3 | 4 | 3 | 4 | |||
10~15 | 22.91 | 1.57 | 1 | 2 | 4 | 2 | 4 | |||
≥15 | 26.2 | 1.8 | 0 | 0 | 4 | 1 | 4 | |||
pH of soil | 5.0~6.5 | 395.5 | 27.18 | 0 | 0 | 3 | 3 | 3 | ||
6.5~7.5 | 1027.39 | 70.62 | 4 | 4 | 4 | 4 | 4 | |||
7.5~8.5 | 31.99 | 2.2 | 2 | 2 | 3 | 3 | 3 |
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Types | Model Name | Calculation Method | Connotation |
---|---|---|---|
Area/ Density | NP | The number of patches in landscape. | |
PD | The density of landscape | ||
MPS | ) | It can be used to characterize landscape fragmentation. | |
Aggregation | COHESION | It reflects the aggregation degree of patches in the landscape. |
Type | CL | OC | FL | GL | SL |
---|---|---|---|---|---|
Appropriate | 16.45 | 27.79 | 1341.78 | 1331.26 | 1346.02 |
Relatively appropriate | 497.91 | 1102.86 | 9.16 | 19.68 | 4.92 |
Inappropriate | 940.52 | 324.23 | 103.94 | 103.94 | 103.94 |
LUCC | 2014 | Prediction of 2030 (Scenario_1) | Optimization of 2030 (Scenario_2) |
---|---|---|---|
CL | 9.93 | 5.07 | 9.93 |
OC | 53.21 | 41.43 | 53.21 |
FL | 556.54 | 503.07 | 579.45 |
SL | 689.23 | 759.39 | 689.23 |
GL | 17.98 | 13.05 | 0.19 |
WB | 16.45 | 16.17 | 22.33 |
BUL | 86.27 | 90.81 | 99.32 |
WL | 24.05 | 25.27 | 0.00 |
BL | 1.22 | 0.61 | 1.22 |
ES | 2014 | Prediction for 2030 (Scenario_1) | Optimization for 2030 (Scenario_2) |
---|---|---|---|
WY(m3) | 2.02 × 108 | 1.86 × 108 | 2.65 × 108 |
CS(t) | 1713.54 × 104 | 1707.39 × 104 | 1707.53 × 104 |
SC(t) | 0.50 × 108 | 0.42 × 108 | 0.56 × 108 |
SLO(t) | 0.66 × 106 | 0.74 × 106 | 0.60 × 106 |
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Yi, Y.; Zhang, C.; Zhu, J.; Zhang, Y.; Sun, H.; Kang, H. Spatio-Temporal Evolution, Prediction and Optimization of LUCC Based on CA-Markov and InVEST Models: A Case Study of Mentougou District, Beijing. Int. J. Environ. Res. Public Health 2022, 19, 2432. https://doi.org/10.3390/ijerph19042432
Yi Y, Zhang C, Zhu J, Zhang Y, Sun H, Kang H. Spatio-Temporal Evolution, Prediction and Optimization of LUCC Based on CA-Markov and InVEST Models: A Case Study of Mentougou District, Beijing. International Journal of Environmental Research and Public Health. 2022; 19(4):2432. https://doi.org/10.3390/ijerph19042432
Chicago/Turabian StyleYi, Yang, Chen Zhang, Jinqi Zhu, Yugang Zhang, Hao Sun, and Hongzhang Kang. 2022. "Spatio-Temporal Evolution, Prediction and Optimization of LUCC Based on CA-Markov and InVEST Models: A Case Study of Mentougou District, Beijing" International Journal of Environmental Research and Public Health 19, no. 4: 2432. https://doi.org/10.3390/ijerph19042432